GenAI Red-Teaming Toolkit · Hacking Project

Automate systematic adversarial testing of LLM applications and produce reproducible, framework-mapped vulnerability reports.

**The problem**
Organizations shipping LLM features rarely test them adversarially before release, leaving jailbreaks, data-leakage paths, harmful-content bypasses, and insecure tool invocation undiscovered until exploited in production. Manual red-teaming does not scale, is inconsistent, and lacks reproducible coverage of the LLM attack surface. There is no unified, automatable harness to systematically probe a model or application against a taxonomy of GenAI attacks and produce audit-ready evidence.

**What you'll build**
Build an extensible red-teaming toolkit that orchestrates automated attack modules — jailbreak templates, prompt-injection payloads, encoding/obfuscation attacks, multi-turn crescendo attacks, data-extraction probes, and tool-abuse tests — against any LLM endpoint. Integrate an attacker LLM that iteratively mutates payloads (genetic/gradient-free optimization), scores success with an LLM-judge, and generates a coverage report mapped to MITRE ATLAS and OWASP LLM Top 10. Package it as a CLI plus CI/CD plugin so red-team runs execute automatically on every model or prompt change.

**Objective** — Automate systematic adversarial testing of LLM applications and produce reproducible, framework-mapped vulnerability reports.

**Expected outcome** — An open-source-style red-teaming CLI/framework with pluggable attack modules, an automated scoring harness, and an HTML/PDF assessment report enumerating findings by ATLAS technique and severity.

**Difficulty** — Advanced — demands adversarial-ML creativity, attacker-LLM orchestration, and evaluation rigor; conceptually broad but buildable on top of existing probing frameworks.

**Primary category** — AI/ML Security (Offensive); Secondary = Penetration Testing, LLM Security, Security Automation

**Security domains** — Primary = Red Team; Secondary = AI Security, Security Research, Purple Team, DevSecOps

**Technical focus** — LLM red-teaming, jailbreak engineering, automated payload mutation, multi-turn/crescendo attacks, LLM-as-judge scoring, data-exfiltration testing, tool/agent abuse, CI/CD security testing, adversarial ML

**Core technologies** — Languages: Python; Frameworks/Libraries: PyRIT, Garak, promptfoo, LangChain, Hugging Face Transformers, Pytest; Security Tools: PyRIT, Garak, Giskard, DeepEval; Cloud/Infra: Docker, GitHub Actions/GitLab CI, cloud LLM endpoints; AI/Data: attacker/judge LLM APIs, attack-prompt corpora, embeddings; Databases: SQLite/PostgreSQL for run history, JSON/Parquet result store

**Where it's valued** — GenAI product vendors, cloud AI platforms, regulated industries (finance, healthcare, defense) requiring AI assurance, AI red-team consultancies

**Careers it builds toward** — AI Red Teamer, LLM Security Researcher, Offensive Security Engineer (AI), AI Assurance Engineer, ML Security Consultant

**Framework alignment**
• MITRE ATLAS: AML.T0051 Prompt Injection, AML.T0054 Jailbreak, AML.T0043 Craft Adversarial Data, AML.T0057 LLM Data Leakage, AML.T0048 External Harms; exercises Reconnaissance, ML Attack Staging, and Execution tactics
• NIST CSF 2.0: Identify (vulnerability discovery), Protect (pre-deployment hardening), Govern (AI assurance/testing policy)
• OWASP: LLM Top 10 — LLM01, LLM02, LLM04 Model Denial of Service, LLM06, LLM08 Excessive Agency, LLM10 Model Theft
• CIS Controls v8: Control 16 Application Software Security, Control 18 Penetration Testing, Control 4 Secure Configuration

**Innovation** — Improved Existing Solution / Enterprise Product — builds on PyRIT/Garak but a polished, CI-integrated, report-generating harness has clear enterprise demand as AI assurance regulation matures. Scores: Innovation 7/10, Startup 8/10, Enterprise-Readiness 7/10, Patent = Possible.